2022

chapter in monograph

english

EN In multiobjective optimization, there is usually not a single optimal solution as result, but a set of so-called Pareto-optimal solutions with different trade-offs between the objectives. Metaheuristics, in particular evolutionary algorithms, have been very successful in solving such problems, because they can simultaneously search for multiple Pareto-optimal solution. However, at some point a decision maker has to be involved to pick the final solution. This chapter reviews and summarizes the research of Prof. Roman Słowiński, members of his Laboratory, and his main collaborators related to the use of the decision maker’s preferences in metaheuristic and evolutionary algorithms.

09.02.2022

297 - 306

10.1007/978-3-030-96318-7_15

https://link.springer.com/chapter/10.1007/978-3-030-96318-7_15

Intelligent Decision Support Systems : Combining Operations Research and Artificial Intelligence - Essays in Honor of Roman Słowiński

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